class_docstr: Calculate the confusion matrix of the model on the given dataset.
  
  Parameters
  ----------
  normalize_display : bool , default: True:
      boolean that determines whether to normalize the values of the matrix in the display.
  n_samples : int , default: 1_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
docstr: Run check.
  
  Returns
  -------
  CheckResult
      value is numpy array of the confusion matrix, displays the confusion matrix
  
  Raises
  ------
  DeepchecksValueError
      If the data is not a Dataset instance with a label
docstr_tok: ["Run","check",".","Returns","--","--","--","-","CheckResult","value","is","numpy","array","of","the","confusion","matrix",",","displays","the","confusion","matrix","Raises","--","--","--","DeepchecksValueError","If","the","data","is","not","a","Dataset","instance","with","a","label"]
code: def run_logic(self, context: Context, dataset_kind) -> CheckResult:
      """Run check.
  
      Returns
      -------
      CheckResult
          value is numpy array of the confusion matrix, displays the confusion matrix
  
      Raises
      ------
      DeepchecksValueError
          If the data is not a Dataset instance with a label
      """
      dataset = context.get_data_by_kind(dataset_kind).sample(
          self.n_samples, random_state=self.random_state
      )
      context.assert_classification_task()
      y_true = dataset.label_col
      y_pred = np.array(
          context.model.predict(dataset.features_columns)
      ).reshape(
          len(y_true),
      )
  
      return run_confusion_matrix_check(
          y_pred, y_true, context.with_display, self.normalize_display
      )
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/confusion_matrix_report.py
imports: import numpy
  from deepchecks.core import CheckResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.abstracts.confusion_matrix_abstract import misclassified_samples_lower_than_condition, run_confusion_matrix_check
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 2 for calling the ConfusionMatrixReport obj's underlying member method code with example usage: obj.run_logic(context, dataset_kind) and returns: run_confusion_matrix_check